Abstract We address multi-view pedestrian detection in a setting where labeled data is collected using a multi-camera setup different from the one used for testing. While recent multi-view pedestrian detectors perform well on the camera rig used for training, their performance declines when applied to a different setup. To facilitate seamless deployment across varied camera rigs, we propose an unsupervised domain adaptation (UDA) method that adapts the model to new rigs without requiring additional labeled data. Specifically, we leverage the mean teacher self-training framework with a novel pseudo-labeling technique tailored to multi-view pedestrian detection. This method achieves state-of-the-art performance on multiple benchmarks, including MultiviewX $$\rightarrow $$ Wildtrack. Unlike previous methods, our approach eliminates the need for external labeled monocular datasets, thereby reducing reliance on labeled data. Extensive evaluations demonstrate the effectiveness of our method and validate key design choices. By enabling robust adaptation across camera setups, our work enhances the practicality of multi-view pedestrian detectors and establishes a strong UDA baseline for future research.

For safe and flexible navigation in multi-robot systems, this paper presents an enhanced and predictive sampling-based trajectory planning approach in complex environments, the Gradient Field-based Dynamic Window Approach (GF-DWA). Building upon the dynamic window approach, the proposed method utilizes gradient information of obstacle distances as a new cost term to anticipate potential collisions. This enhancement enables the robot to improve awareness of obstacles, including those with non-convex shapes. The gradient field is derived from the Gaussian process distance field, which generates both the distance field and gradient field by leveraging Gaussian process regression to model the spatial structure of the environment. Through several obstacle avoidance and fleet collision avoidance scenarios, the proposed GF-DWA is shown to outperform other popular trajectory planning and control methods in terms of safety and flexibility, especially in complex environments with non-convex obstacles.

Cyber-physical systems (CPSs) are often complex and safety-critical, making it both challenging and crucial to ensure that the system’s specifications are met. Simulation-based falsification is a practical testing technique for increasing confidence in a CPS’s correctness, as it only requires that the system be simulated. Reducing the number of computationally intensive simulations needed for falsification is a key concern. In this study, we investigate Bayesian optimization (BO), a sample-efficient approach that learns a surrogate model to capture the relationship between input signal parameterization and specification evaluation. We propose two enhancements to the basic BO for improving falsification: (1) leveraging local surrogate models, and (2) utilizing the user’s prior knowledge. Additionally, we address the formulation of acquisition functions for falsification by proposing and evaluating various alternatives. Our benchmark evaluation demonstrates significant improvements when using local surrogate models in BO for falsifying challenging benchmark examples. Incorporating prior knowledge is found to be especially beneficial when the simulation budget is constrained. For some benchmark problems, the choice of acquisition function noticeably impacts the number of simulations required for successful falsification.

We consider unsupervised domain adaptation (UDA) for semantic segmentation in which the model is trained on a labeled source dataset and adapted to an unlabeled target dataset. Unfortunately, current self-training methods are susceptible to misclassified pseudo-labels resulting from erroneous predictions. Since certain classes are typically associated with less reliable predictions in UDA, reducing the impact of such pseudo-labels without skewing the training towards some classes is notoriously difficult. To this end, we propose an extensive cut-and-paste strategy (ECAP) to leverage reliable pseudo-labels through data augmentation. Specifically, ECAP maintains a memory bank of pseudo-labeled target samples throughout training and cut-and-pastes the most confident ones onto the current training batch. We implement ECAP on top of the recent method MIC and boost its performance on two synthetic-to-real domain adaptation benchmarks. Notably, MIC+ECAP reaches an unprecedented performance of 69.1 mIoU on the Synthia $\rightarrow$ Cityscapes benchmark. Our code is available at https://github.com/ErikBrorsson/ECAP.

Efficient motion planning and control for multiple mobile robots in industrial automation and indoor logistics face challenges such as trajectory generation and collision avoidance in complex environments. We propose a hybrid, sequential method combining Bird’s-Eye-View vision-based continuous Deep Reinforcement Learning (DRL) with Model Predictive Control (MPC). DRL generates candidate trajectories in complex environments, while MPC refines these trajectories to ensure adherence to kinematic and dynamic constraints of the robot, as well as constraints modeling humans’ current and predicted future positions. In this study, the DRL utilizes a Deep Deterministic Policy Gradient model for trajectory generation, demonstrating its capability to navigate non-convex obstacles, a task that might pose challenges for MPC. We demonstrate that the proposed hybrid DRL-MPC model performs favorably in handling new scenarios, computational efficiency, time to destination, and adaptability to complex multi-robot situations when compared to pure DRL or pure MPC approaches.

Falsification is a testing method that aims to increase confidence in the correctness of cyber–physical systems by guiding the search for counterexamples with some optimization algorithm. This method generates input signals for a simulation of the system under test and employs quantitative semantics, which serves as objective functions, to minimize the distance needed to falsify a specification. Various implementations based on different optimization strategies and semantics have been proposed and evaluated in the past. Generally, they assume that an input generator is given. However, this is often not the case in practice and different choices can lead to vastly different outcomes. Therefore, this article introduces and evaluates various parameterizations of input generators, including pulse, sinusoidal, and piecewise signals with different interpolation techniques. These input generators are compared based on their performance on benchmark examples, as well as coverage measures in the space-time and frequency domains. Input generators facilitate the exploration of numerous different input signals within a single falsification problem, making them especially valuable for industrial practitioners seeking to incorporate falsification into their daily development work.

Conventional programming of explicit control code is unsuitable for flexible and collaborative production systems. A model-based approach, which focuses on defining capabilities of a system, instead of specifying how to achieve them, provides an alternative for creating complex, scalable, and reliable systems. This is accomplished through the use of behavior models, and tools such as planning, synthesis, verification, and testing. However, developing such models is not without challenges, as it is possible to overlook or incorrectly specify potential behavior and constraints. This can result in unsolvable planning problems or plans that are invalid for other reasons. When plans are unobtainable, developers receive no feedback, which makes model adjustments a difficult and time-intensive task. This paper recognizes these challenges as crucial barriers for adopting model-based development of intelligent automation systems. To facilitate the development of such systems, an approach for detecting and localizing faults in behavior models is presented. Drawing inspiration from software fault localization techniques, the proposed method involves identifying suspicious resources, variables, and operations. The effectiveness of this approach is illustrated with an example use case.

Testing of Cyber-Physical Systems (CPS) deals with the problem of finding input traces to the systems such that given requirements do not hold. Requirements can be formalized in many different ways; in this work requirements are modeled using Signal Temporal Logic (STL) for which a quantitative measure, or robustness value, can be computed given a requirement together with input and output traces. This value is a measure of how far away the requirement is from not holding and is used to guide falsification procedures for deciding on new input traces to simulate one after the other. When the system under test has multiple requirements, standard approaches are to falsify them one-by-one, or as a conjunction of all requirements, but these approaches do not scale well for industrial-sized problems. In this work we consider testing of systems with multiple requirements by proposing focused multi-requirement falsification. This is a multi-stage approach where the solver tries to sequentially falsify the requirements one-by-one, but for every simulation also evaluate the robustness value for all requirements. After one requirement has been focused long enough, the next requirement to focus is selected by considering the robustness values and trajectory history calculated thus far. Each falsification attempt makes use of a prior sensitivity analysis, which for each requirement estimates the parameters that are unlikely to affect the robustness value, in order to reduce the number of parameters that are used by the optimization solver. The proposed approach is evaluated on a public benchmark example containing a large number of requirements, and includes a comparison of the proposed algorithm against a new suggested baseline method.

The Conflict-Free Electric Vehicle Routing Problem (CF-EVRP) is an extension of the Vehicle Routing Problem (VRP), a combinatorial optimization problem of designing routes for vehicles to visit customers such that a cost function, typically the number of vehicles or the total travelled distance, is minimized. The problem finds many logistics applications, particularly for highly automated logistic systems for material handling. The CF-EVRP involves constraints such as time windows on the delivery to the customers, limited operating range of the vehicles, and limited capacity on the number of vehicles that a road segment can accommodate at the same time. In this paper, the compositional algorithm ComSat for solving the CF-EVRP is presented. The algorithm iterates through the sub-problems until a globally feasible solution is found. The proposed algorithm is implemented using an optimizing SMT-solver and is evaluated against an implementation of a previously presented monolithic model. The soundness and completeness of the algorithm are proven, and it is benchmarked on a set of generated problems and found to be able to solve problems of industrial size.Note to Practitioners—The need to define and solve the CF-EVRP relates to an industrial application where a fleet of autonomous robots navigates in a heterogeneous environment, shared with humans and other vehicles and obstacles. To allow for a low-level trajectory controller to handle dynamic obstacles, like humans and fork-lifts, the CF-EVRP includes capacity constraints on the road segments. This increases the problem complexity, and thus requires to trade off optimality for feasability; this so to get solutions in reasonable time with respect to how long ahead the jobs to schedule are known. The overall problem is to find feasible solutions that satisfy all constraints while avoiding travelling unnecessarily long routes, and at the same time meet the stipulated time-windows to deliver material just-in-time. The compositional algorithm (ComSat) presented in this work is based on the idea to break down the overall scheduling problem into sub-problems that are easier to solve, and then to build a schedule based on the solutions of the sub-problems. ComSat is designed to work well for industrial scenarios where there are good reasons to believe that feasible solutions do exist. This seems a reasonable assumption as in an industrial setting a sufficient number of mobile robots can typically be assumed to be available.

Cyber-physical systems (CPSs) are complex and exhibit both continuous and discrete dynamics, hence it is difficult to guarantee that they satisfy given specifications, i.e., the properties that must be fulfilled by the system. Falsification of temporal logic properties is a testing approach that searches for counterexamples of a given specification that can be used to increase the confidence that a CPS does fulfill its specifications. Falsification can be done using random search methods or optimization methods, both of which have their own benefits and drawbacks. This article introduces two methods that exploit randomness to different degrees: 1) the optimization-free Hybrid-Corner-Random (HCR) and 2) the direct-search method Line-Search Falsification (LSF). HCR combines randomly chosen parameter values with extreme parameter values, which performs surprisingly well on benchmark evaluations. The gradient-free optimization-based LSF optimizes over line segments through a vector of inputs in the$n$-dimensional parameter space. The two methods are compared to the Nelder-Mead and SNOBFIT methods, using a well-known set of benchmark problems and LSF shows better performance than any of the evaluated methods.

Bin packing and bin covering are important optimization problems in many industrial fields, such as packaging, recycling, and food processing. The problem concerns a set of items, each with its own value, that are to be sorted into bins in such a way that the total value of each bin, as measured by the sum of its item values, is not above (for packing) or below (for covering) a given target value. The optimization problem concerns minimizing, for bin packing, or maximizing, for bin covering, the number of bins. This is a combinatorial NP-hard problem, for which true optimal solutions can only be calculated in specific cases, such as when restricted to a small number of items. To get around this problem, many suboptimal approaches exist. This article describes the formulations of the bin packing and covering problems that allow finding the true optimum, for instance, counting hundreds of items using general-purpose MILP-solvers. Also presented are suboptimal solutions that come within less than 10% of the optimum while taking significantly less time to calculate, even ten to 100 times faster, depending on the required accuracy.

Cyber-physical systems (CPSs) are systems with both physical and software components, for example, cars and industrial robots. Since these systems exhibit both discrete and continuous dynamics, they are complex and it is thus difficult to verify that they behave as expected. Falsification of temporal logic properties is an approach to find counterexamples to CPSs by means of simulation. In this article, we propose two additions to enhance the capability of falsification and make it more viable in a large-scale industrial setting. The first addition is a framework for transforming specifications from a signal-based model into signal temporal logic. The second addition is the use of valued Booleans and an additive robust semantics in the falsification process. We evaluate the performance of the additive robust semantics on a set of benchmark models, and we can see that which semantics are preferable depend both on the model and on the specification.

Visual tasks such as automated quality control or packaging require machines to be able to detect and identify objects automatically. In recent years object detection systems using deep learning have made significant advancements achieving better scores at a higher performance. However, these methods typically require large amounts of annotated images for training, which are costly and labor intensive to create. Therefore, it is an attractive alternative to generate the training data synthetically using computer-generated imagery (CGI). In this paper, we investigate how to add realistic texture to CAD objects to generate synthetic data for training of an instance segmentation network (Mask R-CNN) for recognition of manufacturing components. The results show that it is possible to create synthetic data with negligible human effort when using simple procedural materials.

We consider the falsification of temporal logic properties as a method to test complex systems, such as autonomous systems. Since these systems are often safety-critical, it is important to assess whether they fulfill given specifications or not. An adaptive cruise controller for an autonomous car is considered where the closed-loop model has unknown parameters and an important problem is to find parameter combinations for which given specification are broken. We assume that the closed-loop system can be simulated with the known given parameters, no other information is available to the testing framework. The specification, such as, the ability to avoid collisions, is expressed using Signal Temporal Logic (STL). In general, systems consist of a large number of parameters, and it is not possible or feasible to explicitly enumerate all combinations of the parameters. Thus, an optimization-based approach is used to guide the search for parameters that might falsify the specification. However, a key challenge is how to select the objective function such that the falsification of the specification, if it can be falsified, can be falsified using as few simulations as possible. For falsification using optimization it is required to have a measure representing the distance to the falsification of the specification. The way the measure is defined results in different objective functions used during optimization. Different measures have been proposed in the literature and in this paper the properties of the Max Semantics (MAX) and the Mean Alternative Robustness Value (MARV) semantics are discussed. After evaluating these two semantics on an adaptive cruise control example, we discuss their strengths and weaknesses to better understand the properties of the two semantics.

The Eleventh Annual IEEE International Conference on Automation Science and Engineering (CASE 2015) was held on August 24–28, at Elite Park Avenue Hotel in Gothenburg, Sweden. IEEE CASE represents the Flagship Automation Conference of the IEEE Robotics and Automation Society and constitutes the primary forum for cross-industry and multidisciplinary research in automation. The conference theme wasAutomation for a Sustainable Future, a global challenge emphasized at the conference by specific workshops and a number of oral sessions. The focus on sustainability involves energy saving, life science, as well as resource efficiency and reusability.

A challenge for highly configurable products, like vehicles, is that the product system has to support all possible variants that can be configured by a customer. The production system is often highly automated with software in robots, machines and programmable logic controllers that need to be prepared to handle all possible variants. The link between the product and the assembly system can be expressed through operations where each operation models how a part in the bill-of-material is assembled to the product to be built. Typically the operations have precedence constraints that express that certain parts have to be assembled before other parts might be assembled. Given only the precedence constraints a product can generally be assembled in many different ways and through line balancing the operations are assigned to different stations, machines, and/or assembly workers. For configurable products the bill-of-material might be different for each variant, consequently the necessary operations will be different. However, since the operations have precedence constraints we have to make sure that all possible variants can be successfully assembled while still satisfying all precedence constraints. The contribution in this paper is a fully automated novel method that can determine if all possible product variants can be successfully assembled while still satisfying precedence constraints between operations.

In order to develop a computationally efficient implementation of the maximally permissive deadlock avoidance policy (DAP) for complex resource allocation systems (RAS), a recent approach focuses on the identification of a set of critical states of the underlying RAS state-space, referred to as minimal boundary unsafe states. The availability of this information enables an expedient one-step-lookahead scheme that prevents the RAS from reaching outside its safe region. The work presented in this paper seeks to develop a symbolic approach, based on binary decision diagrams (BDDs), for efficiently retrieving the (minimal) boundary unsafe states from the underlying RAS state-space. The presented results clearly demonstrate that symbolic computation enables the deployment of the maximally permissive DAP for complex RAS with very large structure and state-spaces with limited time and memory requirements. Furthermore, the involved computational costs are substantially reduced through the pertinent exploitation of the special structure that exists in the considered problem. Note to Practitioners-A key component of the real-time control of many flexibly automated operations is the management of the allocation of a finite set of reusable resources among a set of concurrently executing processes so that this allocation remains deadlock-free. The corresponding problem is known as deadlock avoidance, and its resolution in a way that retains the sought operational flexibilities has been a challenging problem due to: (i) the inability to easily foresee the longer-term implications of an imminent allocation and (ii) the very large sizes of the relevant state spaces that prevent an online assessment of these implications through exhaustive enumeration. A recent methodology has sought to address these complications through the offline identification and storage of a set of critical states in the underlying state space that renders efficient the safety assessment of any given resource allocation. The results presented in this paper further extend and strengthen this methodology by complementing it with techniques borrowed from the area of symbolic computation; these techniques enable a more compressed representation of the underlying state spaces and of the various subsets and operations that are involved in the pursued computation.

A generic state-vector transition (SVT) model is suggested, including a flexible synchronous composition involving both shared variables and events. This model is analyzed, focusing on properties that are important for supervisor synthesis. A synthesis procedure is then developed for the SVT model, where supervisor guards are generated that guarantee a controllable, nonblocking and maximally permissive supervisor. Novel conditions are introduced, such that more flexible specifications can be applied than earlier suggested for related models. Since the SVT model includes automata and (colored) Petri nets, optionally extended with variables, guards and actions, as special cases, the suggested synthesis approach unifies supervisor synthesis for the main discrete event model classes. Finally, the SVT model is naturally represented and efficiently computed based on binary decision diagrams, and the resulting supervisor guards are easily implemented in industrial control systems.

In this paper, we symbolically represent timed discrete-event systems (TDES), which can be used to efficiently compute the supervisor in the supervisory control theory context. We model a TDES based on timed extended finite automata (TEFAs): an augmentation of extended finite automata (EFAs) by incorporating discrete time into the model. EFAs are ordinary automata extended with discrete variables, where conditional expressions and update functions can be attached to the transitions. The symbolic computations are based on binary decision diagrams (BDDs). We show how TEFAs can be represented by BDDs. The main feature of this approach is that the BDD-based fixed point computations are not based on tick models that have been commonly used in this area, leading to better performance in many cases. The approach has been implemented and applied to a simple case study and several large-scale benchmarks.

In supervisory control theory, a supervisor restricts the plant in order to fulfill given specifications. A problem for larger industrial applications is that the resulting supervisor is not easily implemented and comprehensible for the users. To tackle this problem, an efficient method has recently been introduced to characterize a supervisor by tractable logic conditions, referred to as guards. This approach has been developed for a specific type of automata with variables called extended finite automata (EFAs). An extension of this approach to a more general class of models is presented in this paper. It means that classical supervisory control problems for automata and Petri nets are easily and efficiently solved, but also generalized based on the suggested approach. The synthesis procedure is naturally modeled and efficiently computed based on binary decision diagrams.

The sequential behavior of a manufacturing system results from several constraints introduced during the product, manufacturing, and control logic development. This paper proposes methods and algorithms for automatically representing and visualizing this behavior from various perspectives throughout the development process. A new sequence planning approach is introduced that uses self-contained operations to model the activities and execution constraints. These operations can be represented and visualized from multiple perspectives using a graphical and formal language called Sequences of Operations (SOPs). The operations in a manufacturing system are related to each other in various ways, due to execution constraints expressed by operation pre- and post-conditions. These operation relations include parallel, sequence, arbitrary order, alternative, and hierarchy relations. Based on the SOP language, these relations are identified and visualized in various SOPs and sequences. A software tool, Sequence Planner, has been developed, for organizing the operations into SOPs that visualize only relevant operations and relations.

Supervisory Control Theory (SCT) is a model based framework for automatically synthesizing a supervisor that minimally restricts the behavior of a plant such that a given specification is fulfilled. A problem, which prevents SCT from having a major breakthrough industrially, is that the supervisory synthesis often suffers from the state-space explosion problem. To alleviate this problem, a well-known strategy is to represent and explore the state-space symbolically by using Binary Decision Diagrams. Based on this principle, an efficient symbolic state-space traversal approach, depending on the disjunctive partitioning technique, is presented and the correctness of it is proved. Finally, the efficiency of the presented approach is demonstrated on a set of benchmark examples.

In the supervisory control theory, a supervisor is generated based on given plant and specification models. The supervisor restricts the plant in order to fulfill the specifications. A problem that is typically encountered in industrial applications is that the resulting supervisor is not easily comprehensible for the users. To tackle this problem, we introduce an efficient method to characterize a supervisor by tractable logic conditions, referred to as guards, generated from the models. The guards express under which conditions an event is allowed to occur to fulfill the specifications. To obtain tractable guard expressions, we reduce them by exploiting the structure of the given models. In order to be able to handle complex systems efficiently, the models are symbolically represented by binary decision diagrams and all computations are performed on these data structures. The algorithms have been implemented in a supervisory control tool and applied to an industrially relevant example.

Extended Finite Automata (EFA), i.e., finite automata extended with variables, are a suitable modeling framework for discrete event systems owing to their compactness, resulting from the use of variables. In this paper, we propose a symbolic algorithm that efficiently synthesizes a supervisor for a plant modeled by an EFA and a specification defined by another EFA. The principle of the algorithm is to iteratively strengthen the guards of the plant EFA so that forbidden or blocking states become unreachable in the controlled plant. As a consequence of the algorithm, the controlled behavior is modeled by an EFA having the same structure as the plant EFA, having stronger guards and is shown to be maximally permissive. We illustrate our algorithm via a simple manufacturing example.

In order to obtain a unified information flow from early product design to final production, an integrated framework for product, process and automation design is presented. The framework is based on sequences of operations and includes a formal relation between product properties and process operations. This relation includes liaisons (interfaces) and precedence relations, where the precedence relations generate preconditions for the related process operations. From this information a set of sequences of operations (SOPs) is generated. A formal graphical language for hierarchical operations and SOPs is then introduced and defined based on automata extended with variables. Since the operations are self-contained they can be grouped and viewed from different angles, e.g., from a product or a resource perspective. These multiple views increase the interoperability between different engineering disciplines. A case study is performed on a car manufacturing cell, where the suggested modeling framework is shown to give comprehensible SOPs.

Component-based programming frameworks for industrial control logic development promise to shorten development and modification times, and to reduce programming errors. To get these benefits, it is, however, important that the components are specified and verified to work properly. This work introducesReusable Automation Components(RACs), which contain not only the implementation details but also a formal specification defining the correct use and behaviour of the component. This formal specification uses temporal logic to describe time-related properties and has a special structure developed to meet industrial control needs. The RAC can be formally verified, to determine whether the implementation fulfils the specification or not. A RAC prototype development tool has been developed to demonstrate this capability. The main difference between the RAC and other frameworks for formal verification of control logic is the specification modeling. In RAC, not only the implementation but also the specification is based on the structure and languages of conventional control logic, aiming at being easy to comprehend for control logic engineers. Several industrial examples are discussed in this paper, showing the benefits and potential of the framework.

IEC 61499 is a standard architecture, based on function blocks, for developing distributed control and measurement applications. However, the standard has no formal semantics and different interpretations of the standard have emerged. As a consequence, it is harder to transfer applications between different standard compliant platforms. This paper presents a formal framework for mathematical modeling and comparison of different execution semantics. The framework provides definitions that allow modeling of applications and execution semantics separately. Together, the models can be used to analyze and compare how an application would behave when executed using different execution semantics. In addition, a mathematical model made possible by the framework has been used as a basis for implementation of a runtime environment that can execute applications and a software tool that generates formal models suitable for formal verification, both assuming different execution semantics.

IEC 61499 is a standard architecture, based on function blocks, for developing distributed control and measurement applications. However, the standard has no formal semantics and different interpretations of the standard have emerged. As a consequence, the execution behavior of applications running on different platforms may exhibit different behavior, thus making it harder to transfer applications between the platforms. This paper shows how three different execution semantics, buffered sequential execution model (BSEM), nonpreempted multithreaded (NPMTR), and cyclic buffered execution model (CBEM) can be mathematically defined. The mathematical definitions can be used to analyze an application's behavior when executed using those execution semantics. The mathematical definitions have been used as a basis for implementation of a runtime environment and a software tool that generates formal models suitable for formal verification. Formal verification can be used to help discover execution errors before the application is executed on the factory floor.

Quickly adapting the manufacturing system to the production of new or modified products is critical for manufacturers in order to stay competitive. For flexible manufacturing systems this typically implies modifications of the control programs. In previous work a framework for automatic generation of cell controllers has been developed. In this paper an implementation of the framework is presented. Important properties of the presented implementation are: the information from earlier design phases is reused; automatic code generation for faster development and reduced implementation errors; the supervisory control theory is used to generate control functions that are correct by construction; object oriented principles are used in order to allow the reuse of existing library functions. The implementation is generic in the sense that it may generate control programs for a number of target platforms, but in this paper the focus is on generating a control program for the Java platform. An industrial example of a reconfigurable manufacturing cell has been used in the implementation process and shows that the framework is feasible for large manufacturing systems.

A framework for component based distributed control software is proposed. The primary application for the framework is in distributed control systems. The framework proposes new software components, called automation components that can be hierarchically embedded to produce new components. Automation components are also combined to produce hierarchical component based applications. The framework is independent of the execution platform, however it is shown how an application that is developed using the framework can be executed using IEC 61499 platform. The validity of the framework is evaluated using an industrial example of a reconfigurable manufacturing cell.

The execution model in a new standard for distributed control systems, IEC 61499, is analyzed. It is shown how the same standard compliant application running in two different standard compliant runtime environments may result in completely different behaviors. Thus, to achieve true portability of applications between multiple standard compliant runtime environments a more detailed execution model is necessary. In this paper a new runtime environment, Fuber, is presented along with a formal execution model. In this case the execution model is given as a set of interacting state machines which makes it straightforward to analyze the behavior of the application and runtime together using existing tools for formal verification.

Presents a framework for accommodating human intervention in a computer supervised discrete-event system. The basic mechanism for allowing such hybrid supervision by a computer and a human operator is by switching priorities between events controlled by each according to some specified schedule. To synthesize a computer supervisor under such conditions, a transformation that maps the problem to one that satisfies the model stipulations of the supervisory control theory is presented. The aforementioned framework introduces a parameter that can be tuned to provide for different levels of co-operation between the human and computer supervisors. Several important properties of the resulting supervisors are presented.

Petri net based models for plants and recipes are presented. The plant consists of processors and a transporting system connecting the processors. Processors are typically resources like reactors and tanks, while the transporting system consists of, for example, pipes, valves and pumps. Starting with these models we synthesize a discrete, modular supervisor which coordinates the concurrent execution of a number of recipes within a plant. The main task of the supervisor is to restrict the system's resource booking behavior such as to avoid deadlock situations, that is, situations from which we cannot complete our recipes. Deadlocks can occur when allocating processors or when allocating connections between processors, i.e., resources in the transporting system. These two problems are independent of each other. Thus, a modular supervisor can be synthesized that consists of three modules: a recipe module that controls the plant in a command-response fashion and two deadlock modules. The first coordinates the allocation of processors, and the other coordinates the allocation of resources in the transporting system. This separation of the supervisor into three modules reduces the computational complexity when synthesizing the supervisor and produces a much smaller supervisor. This is very important in industrial sized applications, since deadlock avoidance problems belong to the class of /spl Nscr//spl Pscr/-hard problems. We also discuss similarities between batch systems and flexible manufacturing systems.